Personalized Learning for Global Citizens

Microsoft in Education 1 www.microsoft.com/education/

©2014 Microsoft Corporation

Personalized Learning for Global Citizens

Transformation Framework

Microsoft in Education



About this series The Microsoft in Education Transformation Framework is a

guide for educators and leaders engaged in holistic

education transformation. The critical conversations

needed for effective transformation of education systems

are the focus of this paper series. Each expert author

presents a global perspective on the topic through the

current thinking and evidence from research and practice,

as well as showcase examples. Specifically, the papers

document the contributions of anytime anywhere

approaches to K-12 learning and explore the potential of

new technology for transforming learning outcomes for

students and their communities.



Microsoft in Education Transformation Framework Papers Vision for Anytime Anywhere Learning for All

Enabling Transformation with Strategic Planning, Organizational Capacity, and Sustainability

Quality Assurance: Monitoring and Evaluation to Inform Practice and Leadership

Inclusion: Equitable Access and Accessibility

Public, Private, and Community Partnerships for Employability

Curriculum, Content, and Assessment for the Real World

Personalized Learning for Global Citizens

Learning Communities and Support

Building Leader and Educator Capacity for Transformation

Transforming Learning Environments for Anytime, Anywhere Learning for All

Designing Technology for Efficient and Effective Schools



Table of Contents

About this series.............................................................................................. 2

Microsoft in Education Transformation Framework Papers ................... 3

Summary ........................................................................................................... 5

Personalized Learning: History, Theory, and Meaning ........................... 6

Research on Personalized Learning ............................................................ 7

Active Involvement and Aiming Towards Understanding

Rather than Memorization ........................................................................... 8

Social Participation ......................................................................................... 9

Meaningful Activities and Helping Students Learn to Transfer........... 10

Relating New Information to Prior Knowledge and

Restructuring Prior Knowledge .................................................................. 11

Being Strategic .............................................................................................. 13

Engaging in Self-Regulation and Being Reflective................................. 13

Taking Time to Practice ............................................................................... 15

Developmental and Individual Differences ............................................ 16

Creating Motivated Learners ...................................................................... 18

Realizing Personal Learning ........................................................................ 19

Guiding Questions for personalized learning: ....................................... 21

References ...................................................................................................... 23

Author Bios ..................................................................................................... 29

Acknowledgements ...................................................................................... 29



Summary Personalizing learning means students choosing courses, goals, and

pathways to achieve those goals, enabled by technologies that

customize learning paths for students by way of data dashboards

that help students navigate through their learning. Personalized

learning is made scalable as a result of the capabilities that

technology has reached in recent years. In this paper, we highlight

the research on personalized learning, provide examples of

personalized learning facilitated by technology, and offer guiding

principles for leaders and educators who are planning personalized

learning programs.

UNESCO’s Information and Communications Technology (ICT)

Competency Framework for Teachers also include personalized

learning by asking that teachers “identify a personal professional

learning goal and create a plan for the use of various ICT tools to

accomplish this goal…” (p. 25), preparing them for the work that they

will do with personalizing learning for their own learners. OECD

(2006) describes the imperative for personalized education to

overcome socio-economic, time, and space limitations through

educational policy and practice whereby every student matters and

education opportunities are maximized through fostering learning

skills and motivation.

Personalized learning has resurged in recent years as educators have

recognized the merits of technology’s role in facilitating cost-

effective personalized learning and bringing it to scale. Around the

world, technology is allowing schools to design customizable

learning pathways for individual students and provide data-rich

feedback cycles for teachers and students (Patrick, Kennedy, &

Powell, 2013). Personalized learning is neither a new concept, nor a

complete departure from established education practice. Today,

when educators think of personalized learning, they think of students

choosing courses, goals, and pathways to achieve those goals. They

think about the use of technologies that will allow for the

customization of personalized learning paths for students by way of

data dashboards that help students navigate through their learning.

Personalized learning is made scalable as a result of the capabilities

that technology has reached in recent years. In this paper, we

highlight the research on personalized learning, provide examples of

personalized learning facilitated by technology, and offer guiding

principles for leaders and educators who are planning personalized

learning programs.

Kathryn Kennedy, Joseph R. Freidhoff, Kristen DeBruler

Michigan Virtual Learning

Research Institute

MVU

United States

Personalized education has the potential to help overcome socio-economic, time, and space limitations through educational policy and practice whereby every student matters and

education opportunities are maximized through fostering learning skills and motivation.

- OECD, 2006

Microsoft programs and products

empower personalized learning

environments. Learn more:

Individualized learning services

and resources

Analytics Solutions

Personalized Learning

http://www.microsoft.com/en-us/education/school-leaders/analytics-research-management/default.aspx#fbid=hMePa4I2Qkq

http://www.pil-network.com/HotTopics/personalizedlearning



Personalized Learning: History, Theory, and Meaning In the last few years, personalized learning has become a popular

topic in education circles around the world. Major theories and

hypotheses including cognitivism (Piaget, 1985), constructivism

(Piaget, 1950), universal design for learning (Orkwis & McLane, 1998),

situated learning (Lave & Wenger, 1991), connectivism (Siemens,

2014), differentiation (Tomlinson, 1999), revised Bloom’s taxonomy

(Anderson & Krathwohl, 2001), and recently Depth of Knowledge

levels (Webb, 2005) are often associated with personalized learning.

Many organizations have created their own definitions of

personalized learning. In 2010, the U.S. Department of Education

published the following definition in the National Educational

Technology Plan: “Personalization refers to instruction that is paced

to learning needs, tailored to learning preferences, and tailored to

the specific interests of different learners. In an environment that is

fully personalized, the learning objectives and content as well as the

method and pace may all vary” (p. 38). The International Association

for K-12 Online Learning envisions personalized learning as “tailoring

learning for each student’s strengths, needs, and interests – including

enabling student voice and choice in what, how, when, and where

they learn – to provide flexibility and supports to ensure mastery of

the highest standards possible” (Patrick, Kennedy, & Powell, 2013, p. 4).

In the early 20th century, two educational movements in the United

States helped to set the stage for personalized learning. The first was

the Winnetka Plan of 1919, which focused on the “whole child” and

his/her physical, emotional, social, and intellectual education. The

Winnetka Plan used mastery learning and required the student to

become competent in the content before moving onto the next

concept (Butts & Cremin, 1953; Cubberley, 1947; Gutek, 1986). In

1920, the Dalton Plan introduced the need for balance between a

child’s individual needs and those of the community. The Dalton Plan

provided the opportunity for students to manage their learning time

and incorporated the teacher as a resource rather than a lecturer.

Additionally, the Dalton Plan tailored learning to each student’s

needs, interests, and abilities, promoting their independence and

dependability, while enhancing their social skills as well as their

responsibility toward others (Dewey, 1922). At a similar period in

Europe, Montessori education was first initiated, offering students a

choice of learning activities tailored to their needs and freedom of

movement within the classroom.

Learn more about the Dalton Plan

and how it influenced Montessori

education.

Dalton Plan by Helen Parkhurst

http://www.montessori.org/imc/index.php?option=com_content&view=article&id=222:concept-of-the-dalton-plan&catid=40:imc-members-library-secondary-education&Itemid=75



In 1970, the term personalization was coined by Victor Garcia Hoz in

his work “Personalized Education” (Hoz, 1982; Hoz, 1997). According

to Hoz, personalization is the learner’s journey to developing their

freedom of choice. Hoz believed that the learner should be in control

of his/her life experiences. Similar to the Winnetka and Dalton Plans,

Hoz felt that the learning environment should be cognizant of the

learner’s cognitive, affective, and social-emotional development.

Based on Hoz’s work, personalized education had two objectives:

Learning goals should be created with input from the learner and

should be based on the following personal developmental

characteristics, including learner preferences, creativity, freedom,

originality, autonomy, socialization, and communication; and

Learning environment and activities should be organized around the

learner, and the work should be designed to allow for student

control of learning where the student can create and discover by

using a variety of learning resources. Teachers guide the autonomous

learning of the student.

Research on Personalized Learning Having laid the historical, theoretical, and semantic groundwork for

personalized learning, we next take a look at the important practical

evidence base that exists to support it. A large part of the research

that has been done for personalized learning was compiled by the

UNESCO International Academy of Education’s International Bureau

of Education in their publication on “How Children Learn”

(Vosniadou, 2001). Within this publication is a list of 12 elements of

personalized learning, each of which was supported by research. The

12 elements include (1) active involvement; (2) social participation; (3)

meaningful activities; (4) relating new information to prior

knowledge; (5) being strategic; (6) engaging in self-regulation and

being reflective; (7) restructuring prior knowledge; (8) aiming towards

understanding rather than memorization; (9) helping students learn

to transfer; (10) taking time to practice; (11) developmental and

individual differences; and (12) creating motivated learners. The 12

elements are discussed below in conjunction with their research base.

A few of the elements are combined due to their close relationship

with one another. Also included, where available, are examples of

current practices in schools.



Active Involvement and Aiming

Towards Understanding Rather than Memorization

At its foundation learning requires the active and constructive

involvement of the learner in applied learning situations (Elmore,

Peterson, & McCarthy, 1996; Piaget, 1978; Scardamalia & Bereiter,

1991). The maker movement1, a trending topic around the world that

is inspired by Dewey’s learning by doing (1916) and Papert’s learning

by making (or constructionism) (Papert & Harel, 1991), is an example

of a learner’s active involvement in their educational experience.

From the Homebrew Computer Lab of the 1970s to Code.org’s Hour

of Code, and student events like Imagine Cup, the idea of active

involvement is key to inspiring creativity, self-reliance, problem-

solving, and decision-making in learners (Robinson, 2011). Another

current theme in education that promotes active learning in K-12

learning environments is project-based learning, which encourages

students to explore real-world problems that they are interested in

with the goal of working through possible solutions to help solve the

problems.

For example, a group of seventh graders at King Middle School in

Portland, Maine, worked with science experts to understand what

bacteria exist in the environment and how the bacteria impacts the

students’ lives. As an end product, the students reported their

findings to the community at large in an e-pamphlet on the

beneficial aspects of bacteria (Edutopia.org). The project also

incorporated multiple disciplines, including science, math, language

arts, social studies, art, and multimedia. Interdisciplinary education is

profoundly important and contributes to active learning as well (Torp

& Sage, 1998). Just as with active learning, learning is more authentic

when material is organized around general principles and

explanations, rather than when it is based on the memorization of

isolated facts and procedures (Halpern, 1992; Resnick & Klopfer,

1989; Perkins, 1992). Project based learning and other applied

learning strategies move beyond the standardized testing

assessment structure that focuses on memorization and emphasizes

the learners’ need to come to understand what they are learning.

Assessment companies and consortia such as Pacific Metrics, Smarter

Balanced Assessment Consortium (SBAC), and the Partnership for

Assessment of Readiness for College and Careers (PARCC) are using

1 See makerfaire.com or diy.org

https://www.imaginecup.com/

http://www.makerfaire.com/

http://www.diy.org/



what is known about assessment and cognition research and through

the deployment of technology, developing new technology-enabled

items that allow for better information about students’

understanding (Winter, Burkhardt, Freidhoff, Stimson & Leslie, 2013).

Social Participation Learning is primarily a social activity and participation in the social life

of the school is central for learning to occur (Brown, Collins, & Duguid,

1989; Collins, Brown, & Newman, 1989; Rogoff, 1990; Vygotsky,

1978). According to learning theory, negotiation in learning helps

students make meaning of what they are learning. Put simply,

learning becomes a dialogue between multiple learners attempting

to come to a shared understanding. Social participation in the

classroom/learning environment, in the school as a whole, and even

outside of school has been strongly related to self-efficacy, respect

for diversity, self-confidence, collaborative skills, avoidance of risk

behaviors, and resilience (Billig, 2000). Today, forward-thinking

schools are engaging their students as stakeholders in the school

and providing them a chance to have a voice in systemic

improvement (Holcomb, 2007). Additionally, in some schools,

students are also getting more involved in service learning at the

community level to take part in their civic role as citizens. Service

learning is a way for students to be involved in their community and

to make meaningful contributions to society.

An example of this type of service learning is a group of visually-

impaired elementary and middle schools students in West Virginia

who worked together to raise money for a local animal shelter; to do

this, they produced, packaged, and marketed dog biscuits with all

proceeds going to the Humane Society (West Virginia Department of

Education, 2000). A 3rd/4th grade classroom in Okemos, MI are using

online and community-based social strategies in their efforts to get

the Northern Spring Peeper frog adopted as Michigan’s state

amphibian. What began as a basic classroom lesson on state symbols

exploded to a multi-faceted learning immersion project that, thus far,

has included content in government, language arts, visual arts,

interviewing, communications, and social media. In students’ “Peeper

Promotion” efforts, they have written formal letters, lobbied their local

Representative (who subsequently sponsored a bill), held radio interviews,

drafted fliers and engaged local businesses, campaigned at events,

created T-shirts, and developed a Facebook page.2

2 https://www.facebook.com/MIspringpeeper

https://www.facebook.com/MIspringpeeper



Additionally, and more recently, serious games, a type of game

designed for purposes other than purely entertainment, are

providing a way for students to engage in both meaningful learning

and collaboration with others (Connolly, Boyle, MacArthur, Hainey &

Boyle, 2012). Robotics clubs have also risen in popularity. Robotics

clubs are typically a course or an after-school activity where groups

of students work together to build a single robot. Once their robot is

completed, they travel and represent their communities in

competitions across the country and around the world. In addition,

online learning in general helps to expand students’ social circles,

providing them additional peers beyond those who attend their face-

to-face school. Another example is the Deeper Learning MOOC

where students at High Tech High built an energy efficient house.3

For the activity, students had to work with experts in the area of

green housing, energy efficiency, and construction in order to build

the house. In this capacity, students were working together to learn

more about the topic and then applying their knowledge to a true-

to-life learning situation. In computer science, pair programming is

another situation where social participation helps to further learning.

Paired programming is where students work together to create

computer programs (Cockburn & Williams, 2011). Kodu Game Lab, a

simple visual programming language is used by students working in

teams to create games4. Students decide on a game concept, and

produce the game together, with students taking on specific roles

whether training peers in how to use the tools, creating graphic

elements, programming game mechanics, or creating a pitch to

explain the game to other students in the class.

Meaningful Activities and Helping Students Learn to Transfer People learn best when they participate in activities that are perceived

to be useful in real life and are culturally relevant (Brown, Collins, &

Duguid, 1989; Heath, 1983). This element helps to bridge the gap

between what students are doing in school and what they do at

home and in their life as a whole. Bridging the gap between school

and home life allows for more authentic learning and better transfer

of knowledge to future learning situations. Learning becomes more

meaningful when the lessons are applied to real-life situations and

are easier to transfer to other learning situations (Bruer, 1993;

Bransford, Brown, & Cocking, 1999; Bereiter, 1997). An example of

3 https://www.teachingchannel.org/videos/tiny-house-collaborative-project-hth 4 http://www.kodugamelab.com/

Learn more about High Tech high.

http://www.kodugamelab.com/

https://www.teachingchannel.org/videos/tiny-house-collaborative-project-hth


http://www.hightechhigh.org/about/



These examples illustrate how

schools are embedding 21st century

skills into teaching and learning.

Learn more about the assessment

and teaching of 21st century skills

from the Assessment and Teaching

of 21st-Century Skills (ATC21S).

authentic learning is a Global Issues course that was designed by

Michigan Virtual School (MVS) to bring American students together

with students in England. Within the course, there were co-teachers,

one from MVS and the other from England. By exposing the students

to global issues, such as global warming and hunger, illustrating the

differences in perspectives on how each defined the problem and the

scale of the problem, and examining how it was culturally tied to

where they were located, a deepened conversation between the two

student groups brought the course to life, expanding their learning

beyond the typical textbook. Students at the end of the course had

to create a local impact plan to bring what they learned to the

attention of their community in an active way.

In addition to authentic learning, learning also needs to be culturally

relevant (Ladson-Billings, 1994). The term culturally relevant has also

been labeled culturally appropriate (Au & Jordan, 1981), culturally

congruent (Mohatt & Erickson, 1981), culturally responsive (Au &

Jordan, 1981), and culturally compatible (Jordan, 1985). Within

culturally relevant learning environments, students have access and

equity to learning environments that help them to attain academic

success, are engaged in activities that help them learn about and

develop their understanding of their own culture, and are

encouraged to be critically conscious and question cultural norms,

institutions, and values. Culturally relevant education can and should

be incorporated into all disciplines; an example of this in physical

education courses would be to introduce culturally-based games

including Mulambilwa, an African bowling and running game, Kho-

Kho, a chase game originating in India, Yemari, a Japanese handball

game, and La Pelota, a Mexican ball game (Harbin, 1964).

Relating New Information to Prior

Knowledge and Restructuring Prior Knowledge Learning is in part the acquisition and construction of knowledge built

on the foundation of what is already understood and believed by the

learner (Bransford, 1979; Bransford, Brown, & Cocking, 1999). This is

critical for students because it is key to comprehension, requiring

students to make sense of their learning experiences in the context

of what they already know (Kujawa & Huske, 1995) and recognizing

how each of these experiences builds on one another. When this

http://atc21s.org/index.php/about/

http://atc21s.org/index.php/about/



understanding and recognition does not happen, prior knowledge

can stand in the way of learning something new. To overcome this,

students must learn how to solve internal inconsistencies and

restructure existing conceptions when necessary (Piaget, 1978;

Carretero & Voss, 1994; Driver, Guesne, & Tiberghien, 1985; Schnotz,

Vosniadou, & Carretero, 1999; Vosniadou & Brewer, 1992). An

activity often used to engage students’ prior knowledge is “think-

pair-share” in which students have to think about the topic-at-hand

silently, then pair up with a partner to talk about what they thought

about, and then share out their thinking with the rest of the class

(Lyman, 1981).

An example of this for a technology skills course would be for

students to talk to each other about their prior knowledge of website

design before they design a website, asking such questions as “When

you look at a website, what are some key design features that you

like or dislike? If you could design an ideal website, what features

would you include?” Another example would be students learning a

new coding language. While the specific commands and structures

may be different, many coding languages are based on basic logical

equations (if/then statements) and follow the same logical principles,

meaning that students may draw on the prior knowledge learned

from one coding language to understand the basics and provide a

foundation for learning the new language. Another place where

students can work to build on knowledge is through Minecraft, which

helps students use the knowledge that they have acquired in their

life and use that to take care of themselves in the game, to see to

their basic needs, including food, water, sleep, security, shelter, and

homeostasis. During this practice, their prior knowledge is sometimes

questioned, and they have to restructure it with the new information

they learn from their interaction with the game. Anytime anywhere

access to powerful personal computing environments affords these

engagements when students are ready to build their knowledge

and skill.

Learn more about how Minecraft is

being used in education:

MinecraftEDU

http://minecraftedu.com/



Being Strategic People learn by employing effective and flexible strategies that help

them to understand, reason, memorize, and solve problems (Mayer,

1987; Palincsar & Brown, 1984; White & Frederickson, 1998). One of

the keys to being strategic is thinking critically. Critical thinking is

required for questions that do not have simple answers; in asking

such questions educators can promote critical thinking in learners.

Some learning programs are taking it one step further and preparing

students to be strategic by teaching them computational thinking

(Papert, 1996). Computational thinking for K-12 provides learners the

chance to analyze and logically order data; model data; create data

abstracts and simulations; formulate problems for computer

assistance; identify, test, and implement possible solutions; automate

solutions via algorithmic thinking; and generalize and apply this

process to other problems (Stephenson & Barr, 2011). Several

programs have been developed to foster computational thinking

including (but not limited to) MIT’s Scratch, Carnegie Mellon’s Alice,

Microsoft’s Kodu and University of Kent’s Greenfoot. Code.org, a

non-profit is working to provide widespread and easily accessible

opportunities to learn coding as well. Additional examples of

fostering strategic thinking in schools around the world also includes

mindmapping with technology tools, such as NovaMind Mind Mapper

and online journaling in OneNote. These types of activities have

come to fruition within blended learning spaces around the world

(Ferdig, Cavanaugh, & Freidhoff, 2012).

Engaging in Self-Regulation and

Being Reflective Learners must know how to plan and monitor their learning, how to

set their own learning goals, and how to correct errors (Brown, 1975;

Boekaerts, Pintrich, & Zeidner, 2000; Marton & Booth, 1997). Self-

regulation is a set of knowledge and skills that allows learners to

appropriately reflect upon and respond to their surroundings based

on what they see, hear, touch, taste, and smell, and to compare that

perception with what they already know (Bronson, 2000). This

practice of self-regulating allows learners to figure out how to react

via their thoughts, emotions, and behaviors. Research has shown that

increasing students’ self-regulation results in increased


http://www.onenote.com/



comprehension and achievement in their learning (Mace, Belifior, &

Hutchinson, 2001). Such self-regulation processes include self-

monitoring, self-instruction, self-evaluation, self-reflection, self-

correction, and self-reinforcement. There are a few common strands

across self-regulated learning theories. The first is that learners must

have an active role in developing their skills and knowledge towards

the attainment of their learning goals. The second is that self-

regulation is an iterative process where the learner sets goals,

decides how to achieve those goals, monitors to see how the process

is going, and then changes the course of action if something is not

working effectively. The third idea is that motivation plays a key role

in learning, specifically for self-regulated learning, and external

motivators may need to be used until intrinsic motivation can

develop. As Cavanaugh (2014) points out, “Blended programs are

most effective when they use technology to increase individualization

and opportunity for reflection on learning” (p. 59).

An example of this is within an AP Macroeconomics course at MVS,

students in the course were asked to use various resources in a

shared virtual space or open educational resource (OER) area. The

instructor added resources that presented the content in a variety of

ways and allowed the students to negotiate which resource was most

useful to them.5 A teacher in Zeeland, Michigan, Tara Maynard, was

featured on the MyBlend website6 talking about how she personalizes

learning for her students. She uses a flipped classroom model in her

8th grade math class where students learn content and take notes

outside of the course via videos and other resources, and then when

they get to class, they do assignments, such as projects, to put their

learning to practice. When students have questions, Ms. Maynard is

there to respond and personalize based on the needs of the students.

Ms. Maynard emphasizes that students self-check their understanding

and ask peers and their teacher for help along the way.

5 http://media.mivu.org/mivhs/apmicroecon/APEconomicsOverview/player.html 6 http://myBlend.org

http://media.mivu.org/mivhs/apmicroecon/APEconomicsOverview/player.html

http://myblend.org/



Taking Time to Practice Learning is a complex activity that cannot be constrained to a specific

time limit. It requires considerable and variable time and periods of

deliberate practice to start building expertise in an area (Bransford,

1979; Chase & Simon, 1973; Coles, 1970). Some research emphasizes

that it takes 10,000 hours to become an expert on something

(Ericsson, Krampe, & Tesch-Romer, 1993). Other researchers

emphasize that there are other factors involved beyond deliberate

practice that need to be taken into account (Hambrick, Altmann,

Oswald, Meinz, Gobet, & Campitelli, 2014). Two key factors are time

spent in deliberate practice and the spacing between practice

sessions (to avoid burnout). A key piece to personalized learning is

creating personalized pathways for learners and going beyond the

seat-based time that is allotted in traditional school days. In practice,

this takes the form of students designing their own learning plan

based on their current knowledge, learning needs, and future goals,

allotting the time they need for each content area. Instead, some

states are looking more at competency, mastery, or proficiency-

based paths so that learners can learn at their own pace (Patrick,

Kennedy, & Powell, 2013). A few examples of technologies that could

help students with practicing could be collaborative projects with

peers and mentors in cloud-based conferencing tools. Simulations,

which have a long history in medicine (Cooper & Taqueti, 2004) and

business (Keys & Wolfe, 1990) training, are also excellent examples

that offer unlimited practice on many tasks that are difficult with

physical materials, continuous advances in digital technology also

afford more realistic, complex, and socially connected simulations.

Online learning also allows students mobile access to learning

content 24-7, provides students the opportunity to go at their own

pace, and specifically, asynchronous learning gives them the chance

to practice and repractice, which is something that we have not been

able to replicate in the traditional classroom (Cavanaugh, 2009).



Developmental and Individual Differences Children learn best when their individual differences are taken into

consideration (Case, 1978; Chen et al., 1998; Gardner, 1991; Gardner,

1993). There is a need for learning to be designed to meet the

developmental and individual differences and needs of all students.

One way in which many schools are approaching this is to use

Universal Design for Learning (UDL) which is a framework designed

to meet the needs of all students. There are three principles of UDL

and their subsequent guidelines and checkpoints are detailed below.

The research evidence for all Checkpoints can be found on the UDL

Center website7:

Table. Universal Design for Learning: Principles, Guidelines, and

Checkpoints

Principle Guideline Checkpoints

Provide

multiple

means of

representation

Provide

options for

perception

1.1: Offer ways of customizing the display

of information

1.2: Offer alternatives for auditory

information

1.3: Offer alternatives for visual information

Provide

options for

language,

mathematical

expressions,

and symbols

2.1: Clarify vocabulary and symbols

2.2: Clarify syntax and structure

2.3: Support decoding of text,

mathematical notation, and symbols

2.4: Promote understanding across

languages

2.5: Illustrates through multiple media

Provide

options

for comp

3.1: Activate or supply background

knowledge

3.2: Highlight patters, critical features,

big ideas, and relationships

3.3: Guide information processing,

visualization, and manipulation

7 http://www.udlcenter.org/research/researchevidence

Microsoft education products and

partners facilitate the decoding of

mathematical notation and symbols.

Microsoft Mathematics

Equation Editor

FluidMath

"Children learn best

when their individual

differences are taken into

consideration.”

(Case, 1978; Chen et al., 1998;

Gardner, 1991; Gardner, 1993).

http://www.udlcenter.org/research/researchevidence

http://www.microsoft.com/en-us/download/details.aspx?id=15702

http://www.c-sharpcorner.com/UploadFile/8ea152/insert-math-equations-in-word-2013/

http://www.fluiditysoftware.com/




Provide

multiple

means of

action and

expression

Provide options

for physical

actions

4.1: Vary the methods for response

and navigation

4.2: Optimize access to tools and assistive

technologies

Provide options

for expression

and

communications

5.1: Use multiple media for communication

5.2: Use multiple tools for construction

and composition

5.3: Build fluencies with graduated levels

of support for practice and performance

Provide options

for executive

functions

6.1: Guide appropriate goal-setting

6.2: Support planning and strategy

development

6.3: Facilitate managing information

and resources

6.4: Enhance capacity for monitoring

progress


Provide

multiple

means of

engagement

Provide

options for

recruiting

interest

7.1: Optimize individual choice and

autonomy

7.2: Optimize relevance, value, and

authenticity

7.3: Minimize threats and distractions

Provide

options for

sustaining

effort and

persistence

8.1: Heighten salience of goals and

objectives

8.2: Vary demands and resources to

optimize challenge

8.3: Foster collaboration and community

8.4: Increase mastery-oriented feedback

Provide

options

for self-

regulation

9.1: Promote expectations and beliefs that

optimize motivation

9.2: Facilitate personal coping skills

and strategies

9.3: Develop self-assessment and reflection

New within the last year at Khan Academy is their Learning

Dashboard that allows students to identify their areas of strength

and where they need improvement, as well as see their progress and

customize their learning path.8 At Summit Public Schools, students

create their own playlists that include courses, course content,

and resources for learning in order to master content knowledge.9 At

Michigan Virtual Learning Research Institute (MVLRI), the researchers

are looking at ways to visualize student data to help teachers identify

and help those students who are struggling.

8 https://www.khanacademy.org/about/blog/post/58354379257/introducing-the-

learning-dashboard 9 http://www.activateinstruction.org/story/activate-helps-summit-public-schools-

prepare-self-directed-college-ready-students/

Microsoft tools help teachers

visualize student data in different

ways.

Read about Microsoft’s Power

BI tool. (via TechCrunch)

How can Power Bi be used in

education?

https://www.khanacademy.org/about/blog/post/58354379257/introducing-the-learning-dashboard

https://www.khanacademy.org/about/blog/post/58354379257/introducing-the-learning-dashboard

http://www.activateinstruction.org/story/activate-helps-summit-public-schools-prepare-self-directed-college-ready-students/

http://www.activateinstruction.org/story/activate-helps-summit-public-schools-prepare-self-directed-college-ready-students/

http://techcrunch.com/2014/02/10/microsofts-power-bi-for-office-365-comes-out-of-preview-simplifies-data-analysis-and-visualizations/

http://techcrunch.com/2014/02/10/microsofts-power-bi-for-office-365-comes-out-of-preview-simplifies-data-analysis-and-visualizations/

http://blogs.msdn.com/b/education/archive/2013/07/12/what-does-power-bi-for-office-mean-for-education-customers.aspx

http://blogs.msdn.com/b/education/archive/2013/07/12/what-does-power-bi-for-office-mean-for-education-customers.aspx



Creating Motivated Learners Learning is critically influenced by learner motivation. Teachers can

help students become more motivated learners by their behavior and

the statements they make (Deci & Ryan, 1985; Dweck, 1989; Lepper

& Hodell, 1989; Spaulding, 1992). Some research based strategies for

motivating learners include becoming a role model for student

interest; getting to know students; using examples freely; using a

variety of student-active teaching activities; setting realistic

performance goals; placing appropriate positive emphasis on testing

and grading; being free with praise and constructive criticism; and

giving students as much control over their own learning as possible

(Bain, 2004; Nilson, 2003; DeLong & Winter, 2002). This element is

also important when thinking about authenticity. If students are able

to learn ideas that are connected to their lives and produce

representations of their knowledge in ways that matter, they are

more motivated.

Another way students are personalizing their learning is by creating

paths that works for them. An example of this is Personalized

Learning Environments (PLEs), investigated as an application of

Drexler’s networked learner, and effective for complex collaborative

learning (Drexler, in press). In a PLE, the student selects the tools and

communities that will best meet his or her learning objectives.

Allowing students to choose their own path or have choice in their

learning motivates them to continue learning. The following section

is intended to help practitioners realize personalized learning in their

classrooms and schools based on the research base and examples

presented above.



Realizing Personal Learning Using the research base discussed above, the following provides

components for creating personalized learning environments for K-

12 students.

Create learning environments that allow students to be actively

involved in their learning process. Incorporating project-based

learning (PBL) is just one example of this.

Assess students in meaningful ways where they can apply their

understanding.

Encourage students’ social participation in both

classroom/learning environment spaces, school communities, as

well as the community at large outside of the students’ learning

space.

Engage students in meaningful activities that are culturally

relevant and true-to-life.

Scaffold students to support the transfer of knowledge from

one context to another.

Foster students’ use of their prior knowledge to build an

understanding for new knowledge.

Urge students to think critically about prior knowledge that

does not fit a new concept, and inspire them to restructure their

thinking to arrive at a new understanding.

Nurture and promote students’ use of strategic, critical thinking

skills for solving problems with creative solutions.

Support students’ self-regulative and self-reflective activities.

Compel students to practice when they want to become experts

in a given area.

Give students multiple opportunities for representing, acting,

expressing, and engaging in their learning.

Motivate learners by allowing them to take control of their

learning, using enthusiasm, setting realistic goals, and providing

praise and constructive criticism.

Teach teachers how to use student data to modify learning to

meet the needs of each student.

Integrate technology in seamless ways that will allow for

customizable learning for each student’s individual path.

Microsoft programs support

personalized learning environments.

Two examples are Skype in the

Classroom and the 21st Century

Learning Design.

https://education.skype.com/

https://education.skype.com/

http://www.pil-network.com/pd/21CLD/Overview

http://www.pil-network.com/pd/21CLD/Overview



This summary provides context to personalized learning as seen

by Microsoft’s Transformation Framework for Education:

Personalized learning is a promising path to differentiate learning for

all students and prepare them for college, career and community in

the 21st century (Weber, 2014). In today’s mobile and cloud enabled

personalized learning environments, the technology is adaptive so

students get individually flexible and responsive path, pace, and

pedagogy according to their needs, interests, and choices. The

technology provides data used by teachers in crafting learning plans

for each student. Effective personalized learning environments

provide tools and learning resources that students use in self-

directed and self-paced learning (Patrick, Kennedy, & Powell, 2013).

Learner engagement and independence are core goals. Integrated

and engaging technology tools can amplify knowledge acquisition,

skill development, and application of learning in comprehensive

tasks. Adapting the pace and pedagogy require access to content

and tools for learning anytime, anywhere, and on any device. Because

learning is deepest with guidance and interaction, the content and

tools should be collaborative (Jonassen, 2012).

The U.S. Department of Education’s Race to the Top District-level

grant (RTT-D) awardees provide numerous examples of school

districts making the switch to personalized learning (see Oliver, et al.,

2014). The winning districts had six key strategies that they used to

implement personalized learning, including (1) data and data systems

that allow for longitudinal/historical student data and formative data

for teachers to use to differentiate for each student; (2) curriculum

and teaching that were not time-based and that allowed students to

interact with content in multiple ways; (3) learning materials that

allowed for digital books, Open Educational Resources (OER), virtual

manipulatives, and adaptive tools for personal paths; (4) repurposed

learning facilities that would allow for more flexible learning in

changeable environments; (5) human capital to understand what

personnel is needed and how they are most effectively utilized; and

(6) professional development to arrive at a place together as a

learning team to make learning student-centered and personalized.

Many more schools districts like the RTT-D winners are out there,

moving towards personalized education for each student. Despite

these districts’ major efforts and the efforts of others, personalization

“Personalized learning is a

promising path to

differentiate learning for

all students and prepare

them for college, career

and community in the

21st century.”

- Weber, 2014



of learning is not as pervasive as we would hope it to be. Given the

practical advances and research base, why hasn’t personalized

learning been realized? One of the greatest challenges for

personalization in schools is that it is not implemented in a way that

it can be brought to scale, typically due to such issues as human

capital limitations, lack of access to necessary resources, and

resistance to change, to name a few. Ultimately, personalized

learning can be wholly realized when there is a systemic change

made within schools, districts, and education-at-large; it is our

responsibility as educators to work to put these research-based

elements into motion in our early, middle, and secondary schools

so our students’ true potential can be realized.

Guiding Questions for personalized learning: Does the complete learning experience prepare school leavers

for college, career, and community?

Does the learning environment drive students and teachers to

be expert learners for life?

How adaptive learning is supported with data analytics?

What software accessibility requirements are needed?

What personalized learning requirements are needed for

staff/students?

How are students and teachers enabled with collaborative,

creative, and productive learning?

How is differentiated instruction supported and enabled?

How well are we prepared to mix

school or workstyle with lifestyle?

What does School@home mean for

students?

How can you manage personal

identity, safety and integrate

workstyle with lifestyle?

What is the role of a cloud social

environment at home,

at school?

How do you facilitate

online/blended/mobile Learning?

Roaming and mobility – how do you

enable all/specific devices?

Peer to Peer interactions – where do

they fit into the

learning process?

Edutainment – what are the roles of

class work and games based

learning?

What lifestyle assets should/can be

leveraged?

Are our assumptions on student

access, ownership, connectivity and

lifestyle correct?



References Anderson, L. W., & Krathwohl, D. R., et al (Eds.) (2001) A taxonomy for learning, teaching, and assessing: A

revision of Bloom’s Taxonomy of Educational Objectives. Boston, MA: Allyn & Bacon.

Au, K., & Jordan, C. (1981). Teaching reading to Hawaiian children: Finding a culturally appropriate solution.

In H. Trueba, G. Guthrie, & K. Au (Eds.), Culture and bilingual classroom: Studies in classroom ethnography

(pp. 69–86). Rowley, MA: Newbury House.

Bain, K. (2004). What the best college teachers do. Cambridge, MA: Harvard University Press.

Bereiter, C. (1997). Situated cognition and how to overcome it. In D. Kirshner & J. A. Whitson (Eds.), Situated

cognition: Social, semiotic, and psychological perspectives, p. 281–300. Hillsdale, NJ: Erlbaum.

Billig, S. H. (2000). Research on K-12 school-based service-learning: The evidence builds. Phi Delta Kappan,

81, 658-664.

Boekaerts, M., Pintrich, P., & Zeidner, M. (2000). Handbook of Self-Regulation. New York: Academic Press.

Bransford, J. D. (1979). Human cognition: Learning, understanding and remembering. Belmont, CA,

Wadsworth Publishing Co.

Bransford, T. D., Brown, A. L., & Cocking, R. R., (Eds.). (1999). How people learn: Brain, mind, experience and

school. Washington, DC, National Academy Press.

Bronson, M. B. (2000). Self-regulation in early childhood: Nature and nurture. New York: Guilford.

Brown, A. L. (1975). The development of memory: knowing, knowing about knowing and knowing how to

know. In H. W. Reese, H.W (Ed.), Advances in child development and behavior. Vol. 10. New York: Academic

Press.

Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational

Researcher, 18(1).

Bruer, J.T. (1993). Schools for thought. Cambridge, MA: MIT Press.

Butts, R. F. & Cremin, L. A. (1953). History of education in American culture. New York: Henry Holt and Company.

Carretero M. & Voss, J., (Eds.). (1994). Cognitive and instructional processes in history and the social sciences.

Hillsdale, NJ: Erlbaum.

Case, R. (1978). Implications of developmental psychology for the design of effective instruction. In A. M.

Lesgold, et al., (Eds.), Cognitive psychology and instruction, p. 441–63. New York: Plenum.



Cavanaugh, C. (2014). Blended education for elementary and secondary students. In R. E. Slavin (Ed.), Proven

programs in education: Science, technology, and mathematics (STEM) (pp. 56-59). Thousand Oaks, CA: SAGE.

Cavanaugh, C. (2009). Getting students more learning time online: Distance education in support of expanded

learning time in K-12 schools. Washington, D.C.: Center for American Progress. Retrieved from

http://www.americanprogress.org/wp-content/uploads/issues/2009/05/pdf/distancelearning.pdf

Cavanaugh, C., McCarthy, A., & East, M. (2014, June). An innovation framework for holistic school

transformation: Ten critical conversations for the 21st Century. Published in the Proceedings of ED-MEDIA:

Conference on Educational Multimedia, Hypermedia & Telecommunications. Waynesville, NC: Association for

the Advancement of Computing in Education (AACE).

Chase, W. G., & Simon, H. A. (1973). The mind’s eye in chess. In W. G. Chase (Ed.), Visual information

processing. New York: Academic Press.

Chen, J., Krechevsky, M., & Viens, J. (1998). Building on children’s strengths: The experience of Project Spectrum.

New York, Teachers College, Columbia University.

Cockburn, A., & Williams, L. (2011). The costs and benefits of pair programming. In Extreme programming

examined (pp. 223-243). Boston, MA: Addison-Wesley Longman Publishing Co.

Coles, R. (1970). Uprooted children: The early life of migrant farm workers. New York: Harper & Row.

Collins, A., Brown, J. S., & Newman, S.F. (1989). Cognitive apprenticeship: Teaching the craft of reading, writing

and mathematics. In L. B. Resnick (Ed.), Knowing, learning and instruction: essays in honor of Robert Glaser, p.

453–84. Hillsdale, NJ: Lawrence Erlbaum.

Connolly, T. M., Boyle, E. A., MacArthur, E., Hainey, T., & Boyle, J. M. (2012). A systematic literature review of

empirical evidence on computer games and serious games. Computers & Education, 59, 661-686.

Cooper, J. B., and Taqueti, V. R. (2004). A brief history of the development of mannequin simulators for clinical

education and training. Quality & Safety in Health Care, 13(1), i11-i18. doi: 10.1136/qshc.2004.009886

Cubberley, E. P. (1947). Public education in the United States. Cambridge: The Riverside Press.

Deci, E. L., & Ryan, R. (1985). Intrinsic motivation and self-determination in human behaviour. New York:

Plenum Press.

DeLong, M., & Winter, D. (2002). Learning to teach and teaching to learn mathematics: Resources for

professional development. Washington, D.C.: Mathematical Association of America.

Dewey, E. (1922). The Dalton laboratory plan. E.P. Dutton & Company. Retrieved from

http://ia600308.us.archive.org/7/items/daltonlaboratory00deweiala/daltonlaboratory00deweiala.pdf

http://www.americanprogress.org/wp-content/uploads/issues/2009/05/pdf/distancelearning.pdf

http://ia600308.us.archive.org/7/items/daltonlaboratory00deweiala/daltonlaboratory00deweiala.pdf



Dewey, J. (1916). Democracy and education; An introduction to the philosophy of education. New York:

Macmillan.

Drexler, W. (in press). Personal Learning Environments in K12. In R. Ferdig & K. Kennedy (Eds.), Handbook of

Research on K-12 Online and Blended Learning.

Driver, R., Guesne, E., & Tiberghien, A. (Eds.). (1985). Children’s ideas in science. Milton Keynes, United

Kingdom: Open University Press.

Dweck, C. S. (1989). Motivation. In Lesgold A.; Glaser, R., (Eds.), Foundations for a psychology of education, p.

87–136. Hillsdale, NJ, Erlbaum.

Elmore, R. F., Peterson, P. L., & McCarthy, S. J. 1996. Restructuring in the classroom: Teaching, learning and

school organization. San Francisco, CA: Jossey-Bass.

Ferdig, R. E., Cavanaugh, C., & Freidhoff, J. R. (2012). Lessons learned from blended programs: Experiences and

recommendations from the field. Vienna, VA: iNACOL.

Gardner, H. (1991). The unschooled mind: How children think and how schools should teach . New York:

Basic Books.

Gardner, H. (1993). Multiple intelligences: The theory in practice. New York: Basic Books.

Gutek, G. (1986). Education in the United States: A historical perspective. New Jersey: Prentice Hall 1986.

Halpern, D. F., (Ed.). (1992). Enhancing thinking skills in the sciences and mathematics. Hillsdale, NJ: Erlbaum.

Hambrick, D. Z., Altmann, E. M., Oswald, F. L., Meinz, E. J., Gobet, F., & Campitelli, G. (2014). Accounting for

expert performance: The devil is in the details. Intelligence, 45, 112-114.

Harbin E. O. (1964). Games of many nations. Nashville, TN: Abingdon Press.

Heath, S. B. (1983). Ways with words: Language, life and work in communities and classrooms. Cambridge,

United Kingdom: Cambridge University Press.

Holcomb, E. L. (2007). Students are stakeholders, too!: Including every voice in authentic high school reform.

Thousand Oaks, CA: Sage Publications.

Hoz, V. G. (1982). L’ educazione personalizzata: Individualizzazione e socializzazione nell’insegnamento. Firenze:

Le Monnier.

Hoz, V. G. (1997). Dal fine agli obiettivi dell’educazione personalizzata, (trad. italiana, dall’edizione originale

1995). Palumbo: Palermo.



Jonassen, D. (2012). Meaningful learning with technology. Upper Saddle River, NJ: Allyn & Bacon.

Jordan, C. (1985). Translating culture: From ethnographic information to educational program. Antrhopology

and Education Quarterly 16 (105–123); and Vogt, L., Jordan, C., & Tharp, R. (1987). "Explaining school failure,

producing school success: Two cases." Anthropology and Education Quarterly 18, 276–286.

Keys, B., & Wolfe, J. (1990). The role of management games and simulations in education and research.

Journal of Management, 16(2), 307-336.

Kujawa, S., & Huske, L. (1995). The strategic teaching and reading project guidebook (Rev. ed.). Oak Brook, IL:

North Central Regional Educational Laboratory.

Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge: Cambridge

University Press.

Lepper, M., & Hodell, M. (1989). Intrinsic motivation in the classroom. In C. Ames & R. Ames (Eds.), Research

on motivation in education, Vol. 3, p. 73-105. New York: Academic Press.

Lyman, F. (1981). The responsive classroom discussion: The inclusion of all students. Mainstreaming Digest.

University of Maryland, College Park, MD.

Mace, F. C., Belfiore, P. J., & Hutchinson, J. M. (2001). Operant theory and research on self-regulation. In B. J.

Zimmerman & D. H. Schunk (Eds.), Self-regulated learning and academic achievement: Theoretical perspectives

(2nd ed., pp. 39–65). Mahwah, NJ: Erlbaum.

Marton, F., & Booth, S. (1997). Learning and awareness. Hillsdale, NJ: Erlbaum.

Mayer, R. E. (1987). Educational psychology: a cognitive approach. Boston, MA: Little, Brown.

Mohatt, G., & Erickson, F. (1981). Cultural differences in teaching styles in an Odawa school: A sociolinguistic

approach. In H. Trueba, G. Guthrie, & K. Au (Eds.), Culture and the bilingual classroom: Studies in classroom

ethnography (pp. 105–119). Rowley, MA: Newbury House.

Nilson, L. (2003). Teaching at its best: A research-based resource for college instructors 2nd ed. Bolton, MA:

Anker Publishing.

Centre for Educational Research and Innovation., & SourceOECD (Online service). (2006). Personalising

education. Paris: Organisation for Economic Co-operation and Development.

Oliver, K., Clark, T., Hibbard, L., Kennedy, K., LaFrance, J., Oglesby, J., & Swan, B. (2014). Personalization of

learning: Lessons from RTT-D Winners. Under review. 13 pages.

Orkwis, R, & McLane, K (1998). A curriculum every student can use: Design principles for student access.

ERIC/OSEP Topical Brief No. ED423654. Reston, VA: ERIC/OSEP Special Project.



Palincsar, A. S., & Brown, A. L. (1984). Reciprocal teaching of comprehension monitoring activities. Cognition

and Instruction, 1, p. 117–175.

Papert, S. (1996). An exploration in the space of mathematics education. International Journal of Computers for

Mathematical Learning, 1(1), 95-123.

Patrick, S., Kennedy, K., & Powell, A. (2013). Mean what you say: Defining and integrating personalized, blended

and competency education. Vienna, VA: iNACOL.

Perkins, D. (1992). Smart schools: Better thinking and learning for every child. Riverside, NJ: The Free Press.

Piaget, Jean. (1950). The psychology of intelligence. New York: Routledge.

Piaget, J. (1978). Success and understanding. Cambridge, MA: Harvard University Press.

Piaget, J. (1985). The equilibration of cognitive structure. Chicago, IL: University of Chicago Press.

Resnick, L. B., & Klopfer, L. E., (Eds.). (1989). Toward the thinking curriculum: Current cognitive research.

Alexandria, VA: ASCD Books.

Roach, A. T., Elliott, S. N., & Webb, N. L. (2005). Alignment of an Alternate Assessment with State Academic

Standards Evidence for the Content Validity of the Wisconsin Alternate Assessment. The Journal of Special

Education, 38(4), 218-231.

Robinson, K. (2011). Out of our minds: Learning to be creative. Hoboken, NJ: John Wiley & Sons.

Rogoff, B. (1990). Apprenticeships in thinking: Cognitive development in social context. New York: Oxford

University Press.

Scardamalia, M., & Bereiter, C. (1991). Higher levels of agency for children in knowledge building: A challenge

for the design of new knowledge media. Journal of the Learning Sciences, 1, 37–68.

Schnotz, W., Vosniadou, S., & Carretero, M. (1999). New perspectives on conceptual change. Oxford, United

Kingdom: Elsevier Science.

Siemens, G. (2014). Thoughts on connectivism. Elearnspace Blog. Retrieved from

http://www.elearnspace.org/blog/2014/04/25/thoughts-on-connectivism/

Spaulding, C. L. (1992). Motivation in the classroom. New York: McGraw Hill.

Stephenson, C., & Barr, V. (2011 May). Defining computational thinking for K-12. CSTA Voice, 7(2), 3-4.

Tomlinson, C. A. (1999). The differentiated classroom: Responding to the needs of all learners. Alexandria, VA:

Association for Supervision and Curriculum Development.

http://www.elearnspace.org/blog/2014/04/25/thoughts-on-connectivism/



Torp, L., & Sage, S. (1998). Problems as possibilities: Problem-based learning for K-16 education. Alexandria,

VA: ASCD.

Vosniadou, S. (2001). How children learn. UNESCO International Academy of Education, International Bureau

of Education.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA:

Harvard University Press.

Vosniadou, S., & Brewer, W. F. (1992). Mental models of the earth: A study of conceptual change in childhood.

Cognitive psychology, 24, 535–58.

Walsh, R. (2011). Lifestyle and mental health. American Psychologist, 66(7), 579-592.

Weber, C., Biswell, C., & Behrens, W. (2014). Exploring critical issues in gifted education: A case studies

approach. Waco, TX: Prufrock Press.

West Virginia Department of Education. (2000). West Virginia’s service-learning lesson-plans for K-12

challenged students. Retrieved from https://wvde.state.wv.us/lessons/sllessonplans.pdf

White, B. Y., & Frederickson, J. R. (1998). Inquiry, modeling and metacognition: Making science accessible to all

students. Cognition and Instruction, 16(1), 13–117.

Winter, P. C., Burkhardt, A. K., Freidhoff, J. R., Stimson, R. J., & Leslie, S. C. (2013). Astonishing impact: An

introduction to five computer-based assessment issues. Lansing, MI: Michigan Virtual Learning Research

Institute. Retrieved from http://media.mivu.org/institute/pdf/astonishing_impact.pdf

https://wvde.state.wv.us/lessons/sllessonplans.pdf

http://media.mivu.org/institute/pdf/astonishing_impact.pdf



Author Bios Kathryn Kennedy is a Senior Researcher at the Michigan Virtual Learning Research Institute at MVU. She

received her Ph.D. in curriculum and instruction with a concentration in educational technology from the

University of Florida. Her practical experiences and research interests include pre-service and in-service

teacher professional development for technology integration and instructional design in traditional, blended,

and online learning environments.

Joe Freidhoff is the Executive Director of the Michigan Virtual Learning Research Institute at MVU. He received

his Ph.D. in Educational Psychology and Educational Technology from Michigan State University. His work

focuses on K-12 online learning research, data-driven decision making and optimization, analysis of student

and teacher performance, course evaluation, and state-level policy.

Kristen DeBruler is a researcher at the Michigan Virtual Learning Research Institute at MVU. She received

her Ph.D. in Educational Psychology and Educational Technology from Michigan State University and taught

in the Master of Arts in Educational Technology program at Michigan State University for three years. Her

research focuses on preparing K–12 online teachers and supporting K-12

online students.

Acknowledgements Microsoft in Education gratefully acknowledges the support and participation of the individuals who offered feedback,

expertise, and insights to advance this work. We appreciate the contributions of Aidan McCarthy, Dr. Cathy

Cavanaugh, Alexa Joyce, Dr. Ginno Kelley, Brian Gibson, Beau Bertke, Sean Tierney and Wole Moses.



Microsoft in Education

www.microsoft.com/education/


http://www.microsoft.com/education/

Documents

Personalized Learning for Global Citizens